School of Medicine Publications and Presentations

General Kernel Machine Methods for Multi-Omics Integration and Genome-Wide Association Testing With Related Individuals

Document Type

Article

Publication Date

1-2025

Abstract

Integrating multi-omics data may help researchers understand the genetic underpinnings of complex traits and diseases. However, the best ways to integrate multi-omics data and use them to address pressing scientific questions remain a challenge. One important and topical problem is how to assess the aggregate effect of multiple genomic data types (e.g. genotypes and gene expression levels) on a phenotype, particularly while accommodating routine issues, such as having related subjects' data in analyses. In this paper, we extend an existing composite kernel machine regression model to integrate two multi-omics data types, while accommodating for general correlation structures amongst outcomes. Due to the kernel machine regression framework, our methods allow for the integration of high-dimensional omics data with small, nonlinear, and interactive effects, and accommodation of general study designs. Here, we focus on scientific questions that aim to assess the association between a functional grouping (such as a gene or a pathway) and a quantitative trait of interest. We use a kernel machine regression to integrate the two multi-omics data types, as they may relate to the trait, and perform a global test of association. We demonstrate the advantage of this approach over single data type association tests via simulation. Finally, we apply this method to a large, multi-ethnic data set to investigate how predicted gene expression and rare genetic variation may be related to two platelet traits.

Comments

© 2025 Wiley Periodicals LLC.

https://onlinelibrary.wiley.com/share/DGT39FJ9VEJJ84IFUSKX?target=10.1002/gepi.22610

Publication Title

Genetic epidemiology

DOI

https://doi.org/10.1002/gepi.22610

Academic Level

faculty

Mentor/PI Department

Office of Human Genetics

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